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Merge pull request #13084 from vpisarev:shuffle_optflow_algos

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* moved DIS optical flow from opencv_contrib to opencv, moved TVL1 from opencv to opencv_contrib

* fixed compile warning

* TVL1 optical flow example moved to opencv_contrib
This commit is contained in:
Vadim Pisarevsky 2018-11-09 17:52:06 +03:00 committed by Alexander Alekhin
parent 841741aa52
commit 96bf26611e
23 changed files with 4308 additions and 2633 deletions
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308
modules/video/include/opencv2/video/tracking.hpp
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@ -397,6 +397,29 @@ public:
};
/** @brief Read a .flo file
@param path Path to the file to be loaded
The function readOpticalFlow loads a flow field from a file and returns it as a single matrix.
Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the
flow in the horizontal direction (u), second - vertical (v).
*/
CV_EXPORTS_W Mat readOpticalFlow( const String& path );
/** @brief Write a .flo to disk
@param path Path to the file to be written
@param flow Flow field to be stored
The function stores a flow field in a file, returns true on success, false otherwise.
The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds
to the flow in the horizontal direction (u), second - vertical (v).
*/
CV_EXPORTS_W bool writeOpticalFlow( const String& path, InputArray flow );
/**
Base class for dense optical flow algorithms
*/
class CV_EXPORTS_W DenseOpticalFlow : public Algorithm
{
public:
@ -433,131 +456,6 @@ public:
OutputArray err = cv::noArray()) = 0;
};
/** @brief "Dual TV L1" Optical Flow Algorithm.
The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
@cite Javier2012 .
Here are important members of the class that control the algorithm, which you can set after
constructing the class instance:
- member double tau
Time step of the numerical scheme.
- member double lambda
Weight parameter for the data term, attachment parameter. This is the most relevant
parameter, which determines the smoothness of the output. The smaller this parameter is,
the smoother the solutions we obtain. It depends on the range of motions of the images, so
its value should be adapted to each image sequence.
- member double theta
Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the
attachment and the regularization terms. In theory, it should have a small value in order
to maintain both parts in correspondence. The method is stable for a large range of values
of this parameter.
- member int nscales
Number of scales used to create the pyramid of images.
- member int warps
Number of warpings per scale. Represents the number of times that I1(x+u0) and grad(
I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the
method. It also affects the running time, so it is a compromise between speed and
accuracy.
- member double epsilon
Stopping criterion threshold used in the numerical scheme, which is a trade-off between
precision and running time. A small value will yield more accurate solutions at the
expense of a slower convergence.
- member int iterations
Stopping criterion iterations number used in the numerical scheme.
C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
*/
class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow
{
public:
//! @brief Time step of the numerical scheme
/** @see setTau */
CV_WRAP virtual double getTau() const = 0;
/** @copybrief getTau @see getTau */
CV_WRAP virtual void setTau(double val) = 0;
//! @brief Weight parameter for the data term, attachment parameter
/** @see setLambda */
CV_WRAP virtual double getLambda() const = 0;
/** @copybrief getLambda @see getLambda */
CV_WRAP virtual void setLambda(double val) = 0;
//! @brief Weight parameter for (u - v)^2, tightness parameter
/** @see setTheta */
CV_WRAP virtual double getTheta() const = 0;
/** @copybrief getTheta @see getTheta */
CV_WRAP virtual void setTheta(double val) = 0;
//! @brief coefficient for additional illumination variation term
/** @see setGamma */
CV_WRAP virtual double getGamma() const = 0;
/** @copybrief getGamma @see getGamma */
CV_WRAP virtual void setGamma(double val) = 0;
//! @brief Number of scales used to create the pyramid of images
/** @see setScalesNumber */
CV_WRAP virtual int getScalesNumber() const = 0;
/** @copybrief getScalesNumber @see getScalesNumber */
CV_WRAP virtual void setScalesNumber(int val) = 0;
//! @brief Number of warpings per scale
/** @see setWarpingsNumber */
CV_WRAP virtual int getWarpingsNumber() const = 0;
/** @copybrief getWarpingsNumber @see getWarpingsNumber */
CV_WRAP virtual void setWarpingsNumber(int val) = 0;
//! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time
/** @see setEpsilon */
CV_WRAP virtual double getEpsilon() const = 0;
/** @copybrief getEpsilon @see getEpsilon */
CV_WRAP virtual void setEpsilon(double val) = 0;
//! @brief Inner iterations (between outlier filtering) used in the numerical scheme
/** @see setInnerIterations */
CV_WRAP virtual int getInnerIterations() const = 0;
/** @copybrief getInnerIterations @see getInnerIterations */
CV_WRAP virtual void setInnerIterations(int val) = 0;
//! @brief Outer iterations (number of inner loops) used in the numerical scheme
/** @see setOuterIterations */
CV_WRAP virtual int getOuterIterations() const = 0;
/** @copybrief getOuterIterations @see getOuterIterations */
CV_WRAP virtual void setOuterIterations(int val) = 0;
//! @brief Use initial flow
/** @see setUseInitialFlow */
CV_WRAP virtual bool getUseInitialFlow() const = 0;
/** @copybrief getUseInitialFlow @see getUseInitialFlow */
CV_WRAP virtual void setUseInitialFlow(bool val) = 0;
//! @brief Step between scales (<1)
/** @see setScaleStep */
CV_WRAP virtual double getScaleStep() const = 0;
/** @copybrief getScaleStep @see getScaleStep */
CV_WRAP virtual void setScaleStep(double val) = 0;
//! @brief Median filter kernel size (1 = no filter) (3 or 5)
/** @see setMedianFiltering */
CV_WRAP virtual int getMedianFiltering() const = 0;
/** @copybrief getMedianFiltering @see getMedianFiltering */
CV_WRAP virtual void setMedianFiltering(int val) = 0;
/** @brief Creates instance of cv::DualTVL1OpticalFlow*/
CV_WRAP static Ptr<DualTVL1OpticalFlow> create(
double tau = 0.25,
double lambda = 0.15,
double theta = 0.3,
int nscales = 5,
int warps = 5,
double epsilon = 0.01,
int innnerIterations = 30,
int outerIterations = 10,
double scaleStep = 0.8,
double gamma = 0.0,
int medianFiltering = 5,
bool useInitialFlow = false);
};
/** @brief Creates instance of cv::DenseOpticalFlow
*/
CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm.
*/
@ -599,6 +497,166 @@ public:
int flags = 0);
};
/** @brief Variational optical flow refinement
This class implements variational refinement of the input flow field, i.e.
it uses input flow to initialize the minimization of the following functional:
\f$E(U) = \int_{\Omega} \delta \Psi(E_I) + \gamma \Psi(E_G) + \alpha \Psi(E_S) \f$,
where \f$E_I,E_G,E_S\f$ are color constancy, gradient constancy and smoothness terms
respectively. \f$\Psi(s^2)=\sqrt{s^2+\epsilon^2}\f$ is a robust penalizer to limit the
influence of outliers. A complete formulation and a description of the minimization
procedure can be found in @cite Brox2004
*/
class CV_EXPORTS_W VariationalRefinement : public DenseOpticalFlow
{
public:
/** @brief @ref calc function overload to handle separate horizontal (u) and vertical (v) flow components
(to avoid extra splits/merges) */
CV_WRAP virtual void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v) = 0;
/** @brief Number of outer (fixed-point) iterations in the minimization procedure.
@see setFixedPointIterations */
CV_WRAP virtual int getFixedPointIterations() const = 0;
/** @copybrief getFixedPointIterations @see getFixedPointIterations */
CV_WRAP virtual void setFixedPointIterations(int val) = 0;
/** @brief Number of inner successive over-relaxation (SOR) iterations
in the minimization procedure to solve the respective linear system.
@see setSorIterations */
CV_WRAP virtual int getSorIterations() const = 0;
/** @copybrief getSorIterations @see getSorIterations */
CV_WRAP virtual void setSorIterations(int val) = 0;
/** @brief Relaxation factor in SOR
@see setOmega */
CV_WRAP virtual float getOmega() const = 0;
/** @copybrief getOmega @see getOmega */
CV_WRAP virtual void setOmega(float val) = 0;
/** @brief Weight of the smoothness term
@see setAlpha */
CV_WRAP virtual float getAlpha() const = 0;
/** @copybrief getAlpha @see getAlpha */
CV_WRAP virtual void setAlpha(float val) = 0;
/** @brief Weight of the color constancy term
@see setDelta */
CV_WRAP virtual float getDelta() const = 0;
/** @copybrief getDelta @see getDelta */
CV_WRAP virtual void setDelta(float val) = 0;
/** @brief Weight of the gradient constancy term
@see setGamma */
CV_WRAP virtual float getGamma() const = 0;
/** @copybrief getGamma @see getGamma */
CV_WRAP virtual void setGamma(float val) = 0;
/** @brief Creates an instance of VariationalRefinement
*/
CV_WRAP static Ptr<VariationalRefinement> create();
};
/** @brief DIS optical flow algorithm.
This class implements the Dense Inverse Search (DIS) optical flow algorithm. More
details about the algorithm can be found at @cite Kroeger2016 . Includes three presets with preselected
parameters to provide reasonable trade-off between speed and quality. However, even the slowest preset is
still relatively fast, use DeepFlow if you need better quality and don't care about speed.
This implementation includes several additional features compared to the algorithm described in the paper,
including spatial propagation of flow vectors (@ref getUseSpatialPropagation), as well as an option to
utilize an initial flow approximation passed to @ref calc (which is, essentially, temporal propagation,
if the previous frame's flow field is passed).
*/
class CV_EXPORTS_W DISOpticalFlow : public DenseOpticalFlow
{
public:
enum
{
PRESET_ULTRAFAST = 0,
PRESET_FAST = 1,
PRESET_MEDIUM = 2
};
/** @brief Finest level of the Gaussian pyramid on which the flow is computed (zero level
corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.
@see setFinestScale */
CV_WRAP virtual int getFinestScale() const = 0;
/** @copybrief getFinestScale @see getFinestScale */
CV_WRAP virtual void setFinestScale(int val) = 0;
/** @brief Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well
enough in most cases.
@see setPatchSize */
CV_WRAP virtual int getPatchSize() const = 0;
/** @copybrief getPatchSize @see getPatchSize */
CV_WRAP virtual void setPatchSize(int val) = 0;
/** @brief Stride between neighbor patches. Must be less than patch size. Lower values correspond
to higher flow quality.
@see setPatchStride */
CV_WRAP virtual int getPatchStride() const = 0;
/** @copybrief getPatchStride @see getPatchStride */
CV_WRAP virtual void setPatchStride(int val) = 0;
/** @brief Maximum number of gradient descent iterations in the patch inverse search stage. Higher values
may improve quality in some cases.
@see setGradientDescentIterations */
CV_WRAP virtual int getGradientDescentIterations() const = 0;
/** @copybrief getGradientDescentIterations @see getGradientDescentIterations */
CV_WRAP virtual void setGradientDescentIterations(int val) = 0;
/** @brief Number of fixed point iterations of variational refinement per scale. Set to zero to
disable variational refinement completely. Higher values will typically result in more smooth and
high-quality flow.
@see setGradientDescentIterations */
CV_WRAP virtual int getVariationalRefinementIterations() const = 0;
/** @copybrief getGradientDescentIterations @see getGradientDescentIterations */
CV_WRAP virtual void setVariationalRefinementIterations(int val) = 0;
/** @brief Weight of the smoothness term
@see setVariationalRefinementAlpha */
CV_WRAP virtual float getVariationalRefinementAlpha() const = 0;
/** @copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha */
CV_WRAP virtual void setVariationalRefinementAlpha(float val) = 0;
/** @brief Weight of the color constancy term
@see setVariationalRefinementDelta */
CV_WRAP virtual float getVariationalRefinementDelta() const = 0;
/** @copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta */
CV_WRAP virtual void setVariationalRefinementDelta(float val) = 0;
/** @brief Weight of the gradient constancy term
@see setVariationalRefinementGamma */
CV_WRAP virtual float getVariationalRefinementGamma() const = 0;
/** @copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma */
CV_WRAP virtual void setVariationalRefinementGamma(float val) = 0;
/** @brief Whether to use mean-normalization of patches when computing patch distance. It is turned on
by default as it typically provides a noticeable quality boost because of increased robustness to
illumination variations. Turn it off if you are certain that your sequence doesn't contain any changes
in illumination.
@see setUseMeanNormalization */
CV_WRAP virtual bool getUseMeanNormalization() const = 0;
/** @copybrief getUseMeanNormalization @see getUseMeanNormalization */
CV_WRAP virtual void setUseMeanNormalization(bool val) = 0;
/** @brief Whether to use spatial propagation of good optical flow vectors. This option is turned on by
default, as it tends to work better on average and can sometimes help recover from major errors
introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this
option off can make the output flow field a bit smoother, however.
@see setUseSpatialPropagation */
CV_WRAP virtual bool getUseSpatialPropagation() const = 0;
/** @copybrief getUseSpatialPropagation @see getUseSpatialPropagation */
CV_WRAP virtual void setUseSpatialPropagation(bool val) = 0;
/** @brief Creates an instance of DISOpticalFlow
@param preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM
*/
CV_WRAP static Ptr<DISOpticalFlow> create(int preset = DISOpticalFlow::PRESET_FAST);
};
/** @brief Class used for calculating a sparse optical flow.

72
modules/video/perf/opencl/perf_dis_optflow.cpp Normal file
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@ -0,0 +1,72 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "../perf_precomp.hpp"
#include "opencv2/ts/ocl_perf.hpp"
namespace opencv_test { namespace {
#ifdef HAVE_OPENCL
void MakeArtificialExample(UMat &dst_frame1, UMat &dst_frame2);
typedef tuple<String, Size> DISParams;
typedef TestBaseWithParam<DISParams> DenseOpticalFlow_DIS;
OCL_PERF_TEST_P(DenseOpticalFlow_DIS, perf,
Combine(Values("PRESET_ULTRAFAST", "PRESET_FAST", "PRESET_MEDIUM"), Values(szVGA, sz720p, sz1080p)))
{
DISParams params = GetParam();
// use strings to print preset names in the perf test results:
String preset_string = get<0>(params);
int preset = DISOpticalFlow::PRESET_FAST;
if (preset_string == "PRESET_ULTRAFAST")
preset = DISOpticalFlow::PRESET_ULTRAFAST;
else if (preset_string == "PRESET_FAST")
preset = DISOpticalFlow::PRESET_FAST;
else if (preset_string == "PRESET_MEDIUM")
preset = DISOpticalFlow::PRESET_MEDIUM;
Size sz = get<1>(params);
UMat frame1(sz, CV_8U);
UMat frame2(sz, CV_8U);
UMat flow;
MakeArtificialExample(frame1, frame2);
Ptr<DenseOpticalFlow> algo = DISOpticalFlow::create(preset);
OCL_TEST_CYCLE_N(10)
{
algo->calc(frame1, frame2, flow);
}
SANITY_CHECK_NOTHING();
}
void MakeArtificialExample(UMat &dst_frame1, UMat &dst_frame2)
{
int src_scale = 2;
int OF_scale = 6;
double sigma = dst_frame1.cols / 300;
UMat tmp(Size(dst_frame1.cols / (1 << src_scale), dst_frame1.rows / (1 << src_scale)), CV_8U);
randu(tmp, 0, 255);
resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
Mat displacement_field(Size(dst_frame1.cols / (1 << OF_scale), dst_frame1.rows / (1 << OF_scale)),
CV_32FC2);
randn(displacement_field, 0.0, sigma);
resize(displacement_field, displacement_field, dst_frame2.size(), 0.0, 0.0, INTER_CUBIC);
for (int i = 0; i < displacement_field.rows; i++)
for (int j = 0; j < displacement_field.cols; j++)
displacement_field.at<Vec2f>(i, j) += Vec2f((float)j, (float)i);
remap(dst_frame2, dst_frame2, displacement_field, Mat(), INTER_LINEAR, BORDER_REPLICATE);
}
#endif // HAVE_OPENCL
}} // namespace

111
modules/video/perf/opencl/perf_optflow_dualTVL1.cpp
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@ -1,111 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Fangfang Bai, fangfang@multicorewareinc.com
// Jin Ma, jin@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../perf_precomp.hpp"
#include "opencv2/ts/ocl_perf.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
///////////// OpticalFlow Dual TVL1 ////////////////////////
typedef tuple< tuple<int, double>, bool> OpticalFlowDualTVL1Params;
typedef TestBaseWithParam<OpticalFlowDualTVL1Params> OpticalFlowDualTVL1Fixture;
OCL_PERF_TEST_P(OpticalFlowDualTVL1Fixture, OpticalFlowDualTVL1,
::testing::Combine(
::testing::Values(make_tuple<int, double>(-1, 0.3),
make_tuple<int, double>(3, 0.5)),
::testing::Bool()
)
)
{
Mat frame0 = imread(getDataPath("cv/optflow/RubberWhale1.png"), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty()) << "can't load RubberWhale1.png";
Mat frame1 = imread(getDataPath("cv/optflow/RubberWhale2.png"), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty()) << "can't load RubberWhale2.png";
const Size srcSize = frame0.size();
const OpticalFlowDualTVL1Params params = GetParam();
const tuple<int, double> filteringScale = get<0>(params);
const int medianFiltering = get<0>(filteringScale);
const double scaleStep = get<1>(filteringScale);
const bool useInitFlow = get<1>(params);
double eps = 0.9;
UMat uFrame0; frame0.copyTo(uFrame0);
UMat uFrame1; frame1.copyTo(uFrame1);
UMat uFlow(srcSize, CV_32FC2);
declare.in(uFrame0, uFrame1, WARMUP_READ).out(uFlow, WARMUP_READ);
//create algorithm
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
//set parameters
alg->setScaleStep(scaleStep);
alg->setMedianFiltering(medianFiltering);
if (useInitFlow)
{
//calculate initial flow as result of optical flow
alg->calc(uFrame0, uFrame1, uFlow);
}
//set flag to use initial flow
alg->setUseInitialFlow(useInitFlow);
OCL_TEST_CYCLE()
alg->calc(uFrame0, uFrame1, uFlow);
SANITY_CHECK(uFlow, eps, ERROR_RELATIVE);
}
}
} // namespace opencv_test::ocl
#endif // HAVE_OPENCL

66
modules/video/perf/perf_disflow.cpp Normal file
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@ -0,0 +1,66 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
void MakeArtificialExample(Mat &dst_frame1, Mat &dst_frame2);
typedef tuple<String, Size> DISParams;
typedef TestBaseWithParam<DISParams> DenseOpticalFlow_DIS;
PERF_TEST_P(DenseOpticalFlow_DIS, perf,
Combine(Values("PRESET_ULTRAFAST", "PRESET_FAST", "PRESET_MEDIUM"), Values(szVGA, sz720p, sz1080p)))
{
DISParams params = GetParam();
// use strings to print preset names in the perf test results:
String preset_string = get<0>(params);
int preset = DISOpticalFlow::PRESET_FAST;
if (preset_string == "PRESET_ULTRAFAST")
preset = DISOpticalFlow::PRESET_ULTRAFAST;
else if (preset_string == "PRESET_FAST")
preset = DISOpticalFlow::PRESET_FAST;
else if (preset_string == "PRESET_MEDIUM")
preset = DISOpticalFlow::PRESET_MEDIUM;
Size sz = get<1>(params);
Mat frame1(sz, CV_8U);
Mat frame2(sz, CV_8U);
Mat flow;
MakeArtificialExample(frame1, frame2);
TEST_CYCLE_N(10)
{
Ptr<DenseOpticalFlow> algo = DISOpticalFlow::create(preset);
algo->calc(frame1, frame2, flow);
}
SANITY_CHECK_NOTHING();
}
void MakeArtificialExample(Mat &dst_frame1, Mat &dst_frame2)
{
int src_scale = 2;
int OF_scale = 6;
double sigma = dst_frame1.cols / 300;
Mat tmp(Size(dst_frame1.cols / (1 << src_scale), dst_frame1.rows / (1 << src_scale)), CV_8U);
randu(tmp, 0, 255);
resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
Mat displacement_field(Size(dst_frame1.cols / (1 << OF_scale), dst_frame1.rows / (1 << OF_scale)),
CV_32FC2);
randn(displacement_field, 0.0, sigma);
resize(displacement_field, displacement_field, dst_frame2.size(), 0.0, 0.0, INTER_CUBIC);
for (int i = 0; i < displacement_field.rows; i++)
for (int j = 0; j < displacement_field.cols; j++)
displacement_field.at<Vec2f>(i, j) += Vec2f((float)j, (float)i);
remap(dst_frame2, dst_frame2, displacement_field, Mat(), INTER_LINEAR, BORDER_REPLICATE);
}
}} // namespace

6
modules/video/perf/perf_precomp.hpp
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@ -6,5 +6,11 @@
#include "opencv2/ts.hpp"
#include <opencv2/video.hpp>
#include "opencv2/ts/ts_perf.hpp"
namespace cvtest
{
using namespace perf;
}
#endif

31
modules/video/perf/perf_tvl1optflow.cpp
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@ -1,31 +0,0 @@
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
using namespace perf;
typedef TestBaseWithParam< std::pair<string, string> > ImagePair;
std::pair<string, string> impair(const char* im1, const char* im2)
{
return std::make_pair(string(im1), string(im2));
}
PERF_TEST_P(ImagePair, OpticalFlowDual_TVL1, testing::Values(impair("cv/optflow/RubberWhale1.png", "cv/optflow/RubberWhale2.png")))
{
declare.time(260);
Mat frame1 = imread(getDataPath(GetParam().first), IMREAD_GRAYSCALE);
Mat frame2 = imread(getDataPath(GetParam().second), IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
ASSERT_FALSE(frame2.empty());
Mat flow;
Ptr<DenseOpticalFlow> tvl1 = createOptFlow_DualTVL1();
TEST_CYCLE() tvl1->calc(frame1, frame2, flow);
SANITY_CHECK_NOTHING();
}
}} // namespace

40
modules/video/perf/perf_variational_refinement.cpp Normal file
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@ -0,0 +1,40 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
typedef tuple<Size, int, int> VarRefParams;
typedef TestBaseWithParam<VarRefParams> DenseOpticalFlow_VariationalRefinement;
PERF_TEST_P(DenseOpticalFlow_VariationalRefinement, perf, Combine(Values(szQVGA, szVGA), Values(5, 10), Values(5, 10)))
{
VarRefParams params = GetParam();
Size sz = get<0>(params);
int sorIter = get<1>(params);
int fixedPointIter = get<2>(params);
Mat frame1(sz, CV_8U);
Mat frame2(sz, CV_8U);
Mat flow(sz, CV_32FC2);
randu(frame1, 0, 255);
randu(frame2, 0, 255);
flow.setTo(0.0f);
TEST_CYCLE_N(10)
{
Ptr<VariationalRefinement> var = VariationalRefinement::create();
var->setAlpha(20.0f);
var->setGamma(10.0f);
var->setDelta(5.0f);
var->setSorIterations(sorIter);
var->setFixedPointIterations(fixedPointIter);
var->calc(frame1, frame2, flow);
}
SANITY_CHECK_NOTHING();
}
}} // namespace

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#define EPS 0.001f
#define INF 1E+10F
__kernel void dis_precomputeStructureTensor_hor(__global const short *I0x,
__global const short *I0y,
int patch_size, int patch_stride,
int w, int h, int ws,
__global float *I0xx_aux_ptr,
__global float *I0yy_aux_ptr,
__global float *I0xy_aux_ptr,
__global float *I0x_aux_ptr,
__global float *I0y_aux_ptr)
{
int i = get_global_id(0);
if (i >= h) return;
const __global short *x_row = I0x + i * w;
const __global short *y_row = I0y + i * w;
float sum_xx = 0.0f, sum_yy = 0.0f, sum_xy = 0.0f, sum_x = 0.0f, sum_y = 0.0f;
float8 x_vec = convert_float8(vload8(0, x_row));
float8 y_vec = convert_float8(vload8(0, y_row));
sum_xx = dot(x_vec.lo, x_vec.lo) + dot(x_vec.hi, x_vec.hi);
sum_yy = dot(y_vec.lo, y_vec.lo) + dot(y_vec.hi, y_vec.hi);
sum_xy = dot(x_vec.lo, y_vec.lo) + dot(x_vec.hi, y_vec.hi);
sum_x = dot(x_vec.lo, 1.0f) + dot(x_vec.hi, 1.0f);
sum_y = dot(y_vec.lo, 1.0f) + dot(y_vec.hi, 1.0f);
I0xx_aux_ptr[i * ws] = sum_xx;
I0yy_aux_ptr[i * ws] = sum_yy;
I0xy_aux_ptr[i * ws] = sum_xy;
I0x_aux_ptr[i * ws] = sum_x;
I0y_aux_ptr[i * ws] = sum_y;
int js = 1;
for (int j = patch_size; j < w; j++)
{
short x_val1 = x_row[j];
short x_val2 = x_row[j - patch_size];
short y_val1 = y_row[j];
short y_val2 = y_row[j - patch_size];
sum_xx += (x_val1 * x_val1 - x_val2 * x_val2);
sum_yy += (y_val1 * y_val1 - y_val2 * y_val2);
sum_xy += (x_val1 * y_val1 - x_val2 * y_val2);
sum_x += (x_val1 - x_val2);
sum_y += (y_val1 - y_val2);
if ((j - patch_size + 1) % patch_stride == 0)
{
int index = i * ws + js;
I0xx_aux_ptr[index] = sum_xx;
I0yy_aux_ptr[index] = sum_yy;
I0xy_aux_ptr[index] = sum_xy;
I0x_aux_ptr[index] = sum_x;
I0y_aux_ptr[index] = sum_y;
js++;
}
}
}
__kernel void dis_precomputeStructureTensor_ver(__global const float *I0xx_aux_ptr,
__global const float *I0yy_aux_ptr,
__global const float *I0xy_aux_ptr,
__global const float *I0x_aux_ptr,
__global const float *I0y_aux_ptr,
int patch_size, int patch_stride,
int w, int h, int ws,
__global float *I0xx_ptr,
__global float *I0yy_ptr,
__global float *I0xy_ptr,
__global float *I0x_ptr,
__global float *I0y_ptr)
{
int j = get_global_id(0);
if (j >= ws) return;
float sum_xx, sum_yy, sum_xy, sum_x, sum_y;
sum_xx = sum_yy = sum_xy = sum_x = sum_y = 0.0f;
for (int i = 0; i < patch_size; i++)
{
sum_xx += I0xx_aux_ptr[i * ws + j];
sum_yy += I0yy_aux_ptr[i * ws + j];
sum_xy += I0xy_aux_ptr[i * ws + j];
sum_x += I0x_aux_ptr[i * ws + j];
sum_y += I0y_aux_ptr[i * ws + j];
}
I0xx_ptr[j] = sum_xx;
I0yy_ptr[j] = sum_yy;
I0xy_ptr[j] = sum_xy;
I0x_ptr[j] = sum_x;
I0y_ptr[j] = sum_y;
int is = 1;
for (int i = patch_size; i < h; i++)
{
sum_xx += (I0xx_aux_ptr[i * ws + j] - I0xx_aux_ptr[(i - patch_size) * ws + j]);
sum_yy += (I0yy_aux_ptr[i * ws + j] - I0yy_aux_ptr[(i - patch_size) * ws + j]);
sum_xy += (I0xy_aux_ptr[i * ws + j] - I0xy_aux_ptr[(i - patch_size) * ws + j]);
sum_x += (I0x_aux_ptr[i * ws + j] - I0x_aux_ptr[(i - patch_size) * ws + j]);
sum_y += (I0y_aux_ptr[i * ws + j] - I0y_aux_ptr[(i - patch_size) * ws + j]);
if ((i - patch_size + 1) % patch_stride == 0)
{
I0xx_ptr[is * ws + j] = sum_xx;
I0yy_ptr[is * ws + j] = sum_yy;
I0xy_ptr[is * ws + j] = sum_xy;
I0x_ptr[is * ws + j] = sum_x;
I0y_ptr[is * ws + j] = sum_y;
is++;
}
}
}
__kernel void dis_densification(__global const float *sx, __global const float *sy,
__global const uchar *i0, __global const uchar *i1,
int psz, int pstr,
int w, int h, int ws,
__global float *ux, __global float *uy)
{
int x = get_global_id(0);
int y = get_global_id(1);
int i, j;
if (x >= w || y >= h) return;
int start_is, end_is;
int start_js, end_js;
end_is = min(y / pstr, (h - psz) / pstr);
start_is = max(0, y - psz + pstr) / pstr;
start_is = min(start_is, end_is);
end_js = min(x / pstr, (w - psz) / pstr);
start_js = max(0, x - psz + pstr) / pstr;
start_js = min(start_js, end_js);
float coef, sum_coef = 0.0f;
float sum_Ux = 0.0f;
float sum_Uy = 0.0f;
int i_l, i_u;
int j_l, j_u;
float i_m, j_m, diff;
i = y;
j = x;
/* Iterate through all the patches that overlap the current location (i,j) */
for (int is = start_is; is <= end_is; is++)
for (int js = start_js; js <= end_js; js++)
{
float sx_val = sx[is * ws + js];
float sy_val = sy[is * ws + js];
uchar2 i1_vec1, i1_vec2;
j_m = min(max(j + sx_val, 0.0f), w - 1.0f - EPS);
i_m = min(max(i + sy_val, 0.0f), h - 1.0f - EPS);
j_l = (int)j_m;
j_u = j_l + 1;
i_l = (int)i_m;
i_u = i_l + 1;
i1_vec1 = vload2(0, i1 + i_u * w + j_l);
i1_vec2 = vload2(0, i1 + i_l * w + j_l);
diff = (j_m - j_l) * (i_m - i_l) * i1_vec1.y +
(j_u - j_m) * (i_m - i_l) * i1_vec1.x +
(j_m - j_l) * (i_u - i_m) * i1_vec2.y +
(j_u - j_m) * (i_u - i_m) * i1_vec2.x - i0[i * w + j];
coef = 1 / max(1.0f, fabs(diff));
sum_Ux += coef * sx_val;
sum_Uy += coef * sy_val;
sum_coef += coef;
}
ux[i * w + j] = sum_Ux / sum_coef;
uy[i * w + j] = sum_Uy / sum_coef;
}
#define INIT_BILINEAR_WEIGHTS(Ux, Uy) \
i_I1 = min(max(i + Uy + bsz, i_lower_limit), i_upper_limit); \
j_I1 = min(max(j + Ux + bsz, j_lower_limit), j_upper_limit); \
\
w11 = (i_I1 - floor(i_I1)) * (j_I1 - floor(j_I1)); \
w10 = (i_I1 - floor(i_I1)) * (floor(j_I1) + 1 - j_I1); \
w01 = (floor(i_I1) + 1 - i_I1) * (j_I1 - floor(j_I1)); \
w00 = (floor(i_I1) + 1 - i_I1) * (floor(j_I1) + 1 - j_I1);
float computeSSDMeanNorm(const __global uchar *I0_ptr, const __global uchar *I1_ptr,
int I0_stride, int I1_stride,
float w00, float w01, float w10, float w11, int patch_sz, int i)
{
float sum_diff = 0.0f, sum_diff_sq = 0.0f;
int n = patch_sz * patch_sz;
uchar8 I1_vec1, I1_vec2, I0_vec;
uchar I1_val1, I1_val2;
I0_vec = vload8(0, I0_ptr + i * I0_stride);
I1_vec1 = vload8(0, I1_ptr + i * I1_stride);
I1_vec2 = vload8(0, I1_ptr + (i + 1) * I1_stride);
I1_val1 = I1_ptr[i * I1_stride + 8];
I1_val2 = I1_ptr[(i + 1) * I1_stride + 8];
float8 vec = w00 * convert_float8(I1_vec1) + w01 * convert_float8((uchar8)(I1_vec1.s123, I1_vec1.s4567, I1_val1)) +
w10 * convert_float8(I1_vec2) + w11 * convert_float8((uchar8)(I1_vec2.s123, I1_vec2.s4567, I1_val2)) -
convert_float8(I0_vec);
sum_diff = (dot(vec.lo, 1.0) + dot(vec.hi, 1.0));
sum_diff_sq = (dot(vec.lo, vec.lo) + dot(vec.hi, vec.hi));
sum_diff = sub_group_reduce_add(sum_diff);
sum_diff_sq = sub_group_reduce_add(sum_diff_sq);
return sum_diff_sq - sum_diff * sum_diff / n;
}
__kernel void dis_patch_inverse_search_fwd_1(__global const float *Ux_ptr, __global const float *Uy_ptr,
__global const uchar *I0_ptr, __global const uchar *I1_ptr,
int border_size, int patch_size, int patch_stride,
int w, int h, int ws, int hs, int pyr_level,
__global float *Sx_ptr, __global float *Sy_ptr)
{
int id = get_global_id(0);
int is = id / 8;
if (id >= (hs * 8)) return;
int i = is * patch_stride;
int j = 0;
int psz = patch_size;
int psz2 = psz / 2;
int w_ext = w + 2 * border_size;
int bsz = border_size;
float i_lower_limit = bsz - psz + 1.0f;
float i_upper_limit = bsz + h - 1.0f;
float j_lower_limit = bsz - psz + 1.0f;
float j_upper_limit = bsz + w - 1.0f;
float i_I1, j_I1, w00, w01, w10, w11;
float prev_Ux = Ux_ptr[(i + psz2) * w + j + psz2];
float prev_Uy = Uy_ptr[(i + psz2) * w + j + psz2];
Sx_ptr[is * ws] = prev_Ux;
Sy_ptr[is * ws] = prev_Uy;
j += patch_stride;
int sid = get_sub_group_local_id();
for (int js = 1; js < ws; js++, j += patch_stride)
{
float min_SSD, cur_SSD;
float Ux = Ux_ptr[(i + psz2) * w + j + psz2];
float Uy = Uy_ptr[(i + psz2) * w + j + psz2];
INIT_BILINEAR_WEIGHTS(Ux, Uy);
min_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1,
w, w_ext, w00, w01, w10, w11, psz, sid);
INIT_BILINEAR_WEIGHTS(prev_Ux, prev_Uy);
cur_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1,
w, w_ext, w00, w01, w10, w11, psz, sid);
if (cur_SSD < min_SSD)
{
Ux = prev_Ux;
Uy = prev_Uy;
}
prev_Ux = Ux;
prev_Uy = Uy;
Sx_ptr[is * ws + js] = Ux;
Sy_ptr[is * ws + js] = Uy;
}
}
float3 processPatchMeanNorm(const __global uchar *I0_ptr, const __global uchar *I1_ptr,
const __global short *I0x_ptr, const __global short *I0y_ptr,
int I0_stride, int I1_stride, float w00, float w01, float w10,
float w11, int patch_sz, float x_grad_sum, float y_grad_sum)
{
float sum_diff = 0.0, sum_diff_sq = 0.0;
float sum_I0x_mul = 0.0, sum_I0y_mul = 0.0;
int n = patch_sz * patch_sz;
uchar8 I1_vec1, I1_vec2;
uchar I1_val1, I1_val2;
for (int i = 0; i < 8; i++)
{
uchar8 I0_vec = vload8(0, I0_ptr + i * I0_stride);
I1_vec1 = (i == 0) ? vload8(0, I1_ptr + i * I1_stride) : I1_vec2;
I1_vec2 = vload8(0, I1_ptr + (i + 1) * I1_stride);
I1_val1 = (i == 0) ? I1_ptr[i * I1_stride + patch_sz] : I1_val2;
I1_val2 = I1_ptr[(i + 1) * I1_stride + patch_sz];
float8 vec = w00 * convert_float8(I1_vec1) + w01 * convert_float8((uchar8)(I1_vec1.s123, I1_vec1.s4567, I1_val1)) +
w10 * convert_float8(I1_vec2) + w11 * convert_float8((uchar8)(I1_vec2.s123, I1_vec2.s4567, I1_val2)) -
convert_float8(I0_vec);
sum_diff += (dot(vec.lo, 1.0) + dot(vec.hi, 1.0));
sum_diff_sq += (dot(vec.lo, vec.lo) + dot(vec.hi, vec.hi));
short8 I0x_vec = vload8(0, I0x_ptr + i * I0_stride);
short8 I0y_vec = vload8(0, I0y_ptr + i * I0_stride);
sum_I0x_mul += dot(vec.lo, convert_float4(I0x_vec.lo));
sum_I0x_mul += dot(vec.hi, convert_float4(I0x_vec.hi));
sum_I0y_mul += dot(vec.lo, convert_float4(I0y_vec.lo));
sum_I0y_mul += dot(vec.hi, convert_float4(I0y_vec.hi));
}
float dst_dUx = sum_I0x_mul - sum_diff * x_grad_sum / n;
float dst_dUy = sum_I0y_mul - sum_diff * y_grad_sum / n;
float SSD = sum_diff_sq - sum_diff * sum_diff / n;
return (float3)(SSD, dst_dUx, dst_dUy);
}
__kernel void dis_patch_inverse_search_fwd_2(__global const float *Ux_ptr, __global const float *Uy_ptr,
__global const uchar *I0_ptr, __global const uchar *I1_ptr,
__global const short *I0x_ptr, __global const short *I0y_ptr,
__global const float *xx_ptr, __global const float *yy_ptr,
__global const float *xy_ptr,
__global const float *x_ptr, __global const float *y_ptr,
int border_size, int patch_size, int patch_stride,
int w, int h, int ws, int hs, int num_inner_iter, int pyr_level,
__global float *Sx_ptr, __global float *Sy_ptr)
{
int js = get_global_id(0);
int is = get_global_id(1);
int i = is * patch_stride;
int j = js * patch_stride;
int psz = patch_size;
int psz2 = psz / 2;
int w_ext = w + 2 * border_size;
int bsz = border_size;
int index = is * ws + js;
if (js >= ws || is >= hs) return;
float Ux = Sx_ptr[index];
float Uy = Sy_ptr[index];
float cur_Ux = Ux;
float cur_Uy = Uy;
float cur_xx = xx_ptr[index];
float cur_yy = yy_ptr[index];
float cur_xy = xy_ptr[index];
float detH = cur_xx * cur_yy - cur_xy * cur_xy;
if (fabs(detH) < EPS) detH = EPS;
float invH11 = cur_yy / detH;
float invH12 = -cur_xy / detH;
float invH22 = cur_xx / detH;
float prev_SSD = INF, SSD;
float x_grad_sum = x_ptr[index];
float y_grad_sum = y_ptr[index];
float i_lower_limit = bsz - psz + 1.0f;
float i_upper_limit = bsz + h - 1.0f;
float j_lower_limit = bsz - psz + 1.0f;
float j_upper_limit = bsz + w - 1.0f;
float dUx, dUy, i_I1, j_I1, w00, w01, w10, w11, dx, dy;
float3 res;
for (int t = 0; t < num_inner_iter; t++)
{
INIT_BILINEAR_WEIGHTS(cur_Ux, cur_Uy);
res = processPatchMeanNorm(I0_ptr + i * w + j,
I1_ptr + (int)i_I1 * w_ext + (int)j_I1, I0x_ptr + i * w + j,
I0y_ptr + i * w + j, w, w_ext, w00, w01, w10, w11, psz,
x_grad_sum, y_grad_sum);
SSD = res.x;
dUx = res.y;
dUy = res.z;
dx = invH11 * dUx + invH12 * dUy;
dy = invH12 * dUx + invH22 * dUy;
cur_Ux -= dx;
cur_Uy -= dy;
if (SSD >= prev_SSD)
break;
prev_SSD = SSD;
}
float2 vec = (float2)(cur_Ux - Ux, cur_Uy - Uy);
if (dot(vec, vec) <= (float)(psz * psz))
{
Sx_ptr[index] = cur_Ux;
Sy_ptr[index] = cur_Uy;
}
}
__kernel void dis_patch_inverse_search_bwd_1(__global const uchar *I0_ptr, __global const uchar *I1_ptr,
int border_size, int patch_size, int patch_stride,
int w, int h, int ws, int hs, int pyr_level,
__global float *Sx_ptr, __global float *Sy_ptr)
{
int id = get_global_id(0);
int is = id / 8;
if (id >= (hs * 8)) return;
is = (hs - 1 - is);
int i = is * patch_stride;
int j = (ws - 2) * patch_stride;
int psz = patch_size;
int psz2 = psz / 2;
int w_ext = w + 2 * border_size;
int bsz = border_size;
float i_lower_limit = bsz - psz + 1.0f;
float i_upper_limit = bsz + h - 1.0f;
float j_lower_limit = bsz - psz + 1.0f;
float j_upper_limit = bsz + w - 1.0f;
float i_I1, j_I1, w00, w01, w10, w11;
int sid = get_sub_group_local_id();
for (int js = (ws - 2); js > -1; js--, j -= patch_stride)
{
float min_SSD, cur_SSD;
float2 Ux = vload2(0, Sx_ptr + is * ws + js);
float2 Uy = vload2(0, Sy_ptr + is * ws + js);
INIT_BILINEAR_WEIGHTS(Ux.x, Uy.x);
min_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1,
w, w_ext, w00, w01, w10, w11, psz, sid);
INIT_BILINEAR_WEIGHTS(Ux.y, Uy.y);
cur_SSD = computeSSDMeanNorm(I0_ptr + i * w + j, I1_ptr + (int)i_I1 * w_ext + (int)j_I1,
w, w_ext, w00, w01, w10, w11, psz, sid);
if (cur_SSD < min_SSD)
{
Sx_ptr[is * ws + js] = Ux.y;
Sy_ptr[is * ws + js] = Uy.y;
}
}
}
__kernel void dis_patch_inverse_search_bwd_2(__global const uchar *I0_ptr, __global const uchar *I1_ptr,
__global const short *I0x_ptr, __global const short *I0y_ptr,
__global const float *xx_ptr, __global const float *yy_ptr,
__global const float *xy_ptr,
__global const float *x_ptr, __global const float *y_ptr,
int border_size, int patch_size, int patch_stride,
int w, int h, int ws, int hs, int num_inner_iter,
__global float *Sx_ptr, __global float *Sy_ptr)
{
int js = get_global_id(0);
int is = get_global_id(1);
if (js >= ws || is >= hs) return;
js = (ws - 1 - js);
is = (hs - 1 - is);
int j = js * patch_stride;
int i = is * patch_stride;
int psz = patch_size;
int psz2 = psz / 2;
int w_ext = w + 2 * border_size;
int bsz = border_size;
int index = is * ws + js;
float Ux = Sx_ptr[index];
float Uy = Sy_ptr[index];
float cur_Ux = Ux;
float cur_Uy = Uy;
float cur_xx = xx_ptr[index];
float cur_yy = yy_ptr[index];
float cur_xy = xy_ptr[index];
float detH = cur_xx * cur_yy - cur_xy * cur_xy;
if (fabs(detH) < EPS) detH = EPS;
float invH11 = cur_yy / detH;
float invH12 = -cur_xy / detH;
float invH22 = cur_xx / detH;
float prev_SSD = INF, SSD;
float x_grad_sum = x_ptr[index];
float y_grad_sum = y_ptr[index];
float i_lower_limit = bsz - psz + 1.0f;
float i_upper_limit = bsz + h - 1.0f;
float j_lower_limit = bsz - psz + 1.0f;
float j_upper_limit = bsz + w - 1.0f;
float dUx, dUy, i_I1, j_I1, w00, w01, w10, w11, dx, dy;
float3 res;
for (int t = 0; t < num_inner_iter; t++)
{
INIT_BILINEAR_WEIGHTS(cur_Ux, cur_Uy);
res = processPatchMeanNorm(I0_ptr + i * w + j,
I1_ptr + (int)i_I1 * w_ext + (int)j_I1, I0x_ptr + i * w + j,
I0y_ptr + i * w + j, w, w_ext, w00, w01, w10, w11, psz,
x_grad_sum, y_grad_sum);
SSD = res.x;
dUx = res.y;
dUy = res.z;
dx = invH11 * dUx + invH12 * dUy;
dy = invH12 * dUx + invH22 * dUy;
cur_Ux -= dx;
cur_Uy -= dy;
if (SSD >= prev_SSD)
break;
prev_SSD = SSD;
}
float2 vec = (float2)(cur_Ux - Ux, cur_Uy - Uy);
if ((dot(vec, vec)) <= (float)(psz * psz))
{
Sx_ptr[index] = cur_Ux;
Sy_ptr[index] = cur_Uy;
}
}

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@ -1,378 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Jin Ma jin@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
__kernel void centeredGradientKernel(__global const float* src_ptr, int src_col, int src_row, int src_step,
__global float* dx, __global float* dy, int d_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if((x < src_col)&&(y < src_row))
{
int src_x1 = (x + 1) < (src_col -1)? (x + 1) : (src_col - 1);
int src_x2 = (x - 1) > 0 ? (x -1) : 0;
dx[y * d_step+ x] = 0.5f * (src_ptr[y * src_step + src_x1] - src_ptr[y * src_step+ src_x2]);
int src_y1 = (y+1) < (src_row - 1) ? (y + 1) : (src_row - 1);
int src_y2 = (y - 1) > 0 ? (y - 1) : 0;
dy[y * d_step+ x] = 0.5f * (src_ptr[src_y1 * src_step + x] - src_ptr[src_y2 * src_step+ x]);
}
}
inline float bicubicCoeff(float x_)
{
float x = fabs(x_);
if (x <= 1.0f)
return x * x * (1.5f * x - 2.5f) + 1.0f;
else if (x < 2.0f)
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
else
return 0.0f;
}
__kernel void warpBackwardKernel(__global const float* I0, int I0_step, int I0_col, int I0_row,
image2d_t tex_I1, image2d_t tex_I1x, image2d_t tex_I1y,
__global const float* u1, int u1_step,
__global const float* u2,
__global float* I1w,
__global float* I1wx, /*int I1wx_step,*/
__global float* I1wy, /*int I1wy_step,*/
__global float* grad, /*int grad_step,*/
__global float* rho,
int I1w_step,
int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I0_col&&y < I0_row)
{
//float u1Val = u1(y, x);
float u1Val = u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
//float u2Val = u2(y, x);
float u2Val = u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
float wx = x + u1Val;
float wy = y + u2Val;
int xmin = ceil(wx - 2.0f);
int xmax = floor(wx + 2.0f);
int ymin = ceil(wy - 2.0f);
int ymax = floor(wy + 2.0f);
float sum = 0.0f;
float sumx = 0.0f;
float sumy = 0.0f;
float wsum = 0.0f;
sampler_t sampleri = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
for (int cy = ymin; cy <= ymax; ++cy)
{
for (int cx = xmin; cx <= xmax; ++cx)
{
float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
//sum += w * tex2D(tex_I1 , cx, cy);
int2 cood = (int2)(cx, cy);
sum += w * read_imagef(tex_I1, sampleri, cood).x;
//sumx += w * tex2D(tex_I1x, cx, cy);
sumx += w * read_imagef(tex_I1x, sampleri, cood).x;
//sumy += w * tex2D(tex_I1y, cx, cy);
sumy += w * read_imagef(tex_I1y, sampleri, cood).x;
wsum += w;
}
}
float coeff = 1.0f / wsum;
float I1wVal = sum * coeff;
float I1wxVal = sumx * coeff;
float I1wyVal = sumy * coeff;
I1w[y * I1w_step + x] = I1wVal;
I1wx[y * I1w_step + x] = I1wxVal;
I1wy[y * I1w_step + x] = I1wyVal;
float Ix2 = I1wxVal * I1wxVal;
float Iy2 = I1wyVal * I1wyVal;
// store the |Grad(I1)|^2
grad[y * I1w_step + x] = Ix2 + Iy2;
// compute the constant part of the rho function
float I0Val = I0[y * I0_step + x];
rho[y * I1w_step + x] = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
inline float readImage(__global const float *image, int x, int y, int rows, int cols, int elemCntPerRow)
{
int i0 = clamp(x, 0, cols - 1);
int j0 = clamp(y, 0, rows - 1);
return image[j0 * elemCntPerRow + i0];
}
__kernel void warpBackwardKernelNoImage2d(__global const float* I0, int I0_step, int I0_col, int I0_row,
__global const float* tex_I1, __global const float* tex_I1x, __global const float* tex_I1y,
__global const float* u1, int u1_step,
__global const float* u2,
__global float* I1w,
__global float* I1wx, /*int I1wx_step,*/
__global float* I1wy, /*int I1wy_step,*/
__global float* grad, /*int grad_step,*/
__global float* rho,
int I1w_step,
int u2_step,
int I1_step,
int I1x_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I0_col&&y < I0_row)
{
//float u1Val = u1(y, x);
float u1Val = u1[y * u1_step + x];
//float u2Val = u2(y, x);
float u2Val = u2[y * u2_step + x];
float wx = x + u1Val;
float wy = y + u2Val;
int xmin = ceil(wx - 2.0f);
int xmax = floor(wx + 2.0f);
int ymin = ceil(wy - 2.0f);
int ymax = floor(wy + 2.0f);
float sum = 0.0f;
float sumx = 0.0f;
float sumy = 0.0f;
float wsum = 0.0f;
for (int cy = ymin; cy <= ymax; ++cy)
{
for (int cx = xmin; cx <= xmax; ++cx)
{
float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
int2 cood = (int2)(cx, cy);
sum += w * readImage(tex_I1, cood.x, cood.y, I0_col, I0_row, I1_step);
sumx += w * readImage(tex_I1x, cood.x, cood.y, I0_col, I0_row, I1x_step);
sumy += w * readImage(tex_I1y, cood.x, cood.y, I0_col, I0_row, I1x_step);
wsum += w;
}
}
float coeff = 1.0f / wsum;
float I1wVal = sum * coeff;
float I1wxVal = sumx * coeff;
float I1wyVal = sumy * coeff;
I1w[y * I1w_step + x] = I1wVal;
I1wx[y * I1w_step + x] = I1wxVal;
I1wy[y * I1w_step + x] = I1wyVal;
float Ix2 = I1wxVal * I1wxVal;
float Iy2 = I1wyVal * I1wyVal;
// store the |Grad(I1)|^2
grad[y * I1w_step + x] = Ix2 + Iy2;
// compute the constant part of the rho function
float I0Val = I0[y * I0_step + x];
rho[y * I1w_step + x] = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
__kernel void estimateDualVariablesKernel(__global const float* u1, int u1_col, int u1_row, int u1_step,
__global const float* u2,
__global float* p11, int p11_step,
__global float* p12,
__global float* p21,
__global float* p22,
float taut,
int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < u1_col && y < u1_row)
{
int src_x1 = (x + 1) < (u1_col - 1) ? (x + 1) : (u1_col - 1);
float u1x = u1[(y + u1_offset_y) * u1_step + src_x1 + u1_offset_x] - u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
int src_y1 = (y + 1) < (u1_row - 1) ? (y + 1) : (u1_row - 1);
float u1y = u1[(src_y1 + u1_offset_y) * u1_step + x + u1_offset_x] - u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
int src_x2 = (x + 1) < (u1_col - 1) ? (x + 1) : (u1_col - 1);
float u2x = u2[(y + u2_offset_y) * u2_step + src_x2 + u2_offset_x] - u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
int src_y2 = (y + 1) < (u1_row - 1) ? (y + 1) : (u1_row - 1);
float u2y = u2[(src_y2 + u2_offset_y) * u2_step + x + u2_offset_x] - u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
float g1 = hypot(u1x, u1y);
float g2 = hypot(u2x, u2y);
float ng1 = 1.0f + taut * g1;
float ng2 = 1.0f + taut * g2;
p11[y * p11_step + x] = (p11[y * p11_step + x] + taut * u1x) / ng1;
p12[y * p11_step + x] = (p12[y * p11_step + x] + taut * u1y) / ng1;
p21[y * p11_step + x] = (p21[y * p11_step + x] + taut * u2x) / ng2;
p22[y * p11_step + x] = (p22[y * p11_step + x] + taut * u2y) / ng2;
}
}
inline float divergence(__global const float* v1, __global const float* v2, int y, int x, int v1_step, int v2_step)
{
if (x > 0 && y > 0)
{
float v1x = v1[y * v1_step + x] - v1[y * v1_step + x - 1];
float v2y = v2[y * v2_step + x] - v2[(y - 1) * v2_step + x];
return v1x + v2y;
}
else
{
if (y > 0)
return v1[y * v1_step + 0] + v2[y * v2_step + 0] - v2[(y - 1) * v2_step + 0];
else
{
if (x > 0)
return v1[0 * v1_step + x] - v1[0 * v1_step + x - 1] + v2[0 * v2_step + x];
else
return v1[0 * v1_step + 0] + v2[0 * v2_step + 0];
}
}
}
__kernel void estimateUKernel(__global const float* I1wx, int I1wx_col, int I1wx_row, int I1wx_step,
__global const float* I1wy, /*int I1wy_step,*/
__global const float* grad, /*int grad_step,*/
__global const float* rho_c, /*int rho_c_step,*/
__global const float* p11, /*int p11_step,*/
__global const float* p12, /*int p12_step,*/
__global const float* p21, /*int p21_step,*/
__global const float* p22, /*int p22_step,*/
__global float* u1, int u1_step,
__global float* u2,
__global float* error, float l_t, float theta, int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y,
char calc_error)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I1wx_col && y < I1wx_row)
{
float I1wxVal = I1wx[y * I1wx_step + x];
float I1wyVal = I1wy[y * I1wx_step + x];
float gradVal = grad[y * I1wx_step + x];
float u1OldVal = u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
float u2OldVal = u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
float rho = rho_c[y * I1wx_step + x] + (I1wxVal * u1OldVal + I1wyVal * u2OldVal);
// estimate the values of the variable (v1, v2) (thresholding operator TH)
float d1 = 0.0f;
float d2 = 0.0f;
if (rho < -l_t * gradVal)
{
d1 = l_t * I1wxVal;
d2 = l_t * I1wyVal;
}
else if (rho > l_t * gradVal)
{
d1 = -l_t * I1wxVal;
d2 = -l_t * I1wyVal;
}
else if (gradVal > 1.192092896e-07f)
{
float fi = -rho / gradVal;
d1 = fi * I1wxVal;
d2 = fi * I1wyVal;
}
float v1 = u1OldVal + d1;
float v2 = u2OldVal + d2;
// compute the divergence of the dual variable (p1, p2)
float div_p1 = divergence(p11, p12, y, x, I1wx_step, I1wx_step);
float div_p2 = divergence(p21, p22, y, x, I1wx_step, I1wx_step);
// estimate the values of the optical flow (u1, u2)
float u1NewVal = v1 + theta * div_p1;
float u2NewVal = v2 + theta * div_p2;
u1[(y + u1_offset_y) * u1_step + x + u1_offset_x] = u1NewVal;
u2[(y + u2_offset_y) * u2_step + x + u2_offset_x] = u2NewVal;
if(calc_error)
{
float n1 = (u1OldVal - u1NewVal) * (u1OldVal - u1NewVal);
float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
error[y * I1wx_step + x] = n1 + n2;
}
}
}

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include<iostream>
#include<fstream>
namespace cv {
const float FLOW_TAG_FLOAT = 202021.25f;
const char *FLOW_TAG_STRING = "PIEH";
CV_EXPORTS_W Mat readOpticalFlow( const String& path )
{
Mat_<Point2f> flow;
std::ifstream file(path.c_str(), std::ios_base::binary);
if ( !file.good() )
return flow; // no file - return empty matrix
float tag;
file.read((char*) &tag, sizeof(float));
if ( tag != FLOW_TAG_FLOAT )
return flow;
int width, height;
file.read((char*) &width, 4);
file.read((char*) &height, 4);
flow.create(height, width);
for ( int i = 0; i < flow.rows; ++i )
{
for ( int j = 0; j < flow.cols; ++j )
{
Point2f u;
file.read((char*) &u.x, sizeof(float));
file.read((char*) &u.y, sizeof(float));
if ( !file.good() )
{
flow.release();
return flow;
}
flow(i, j) = u;
}
}
file.close();
return flow;
}
CV_EXPORTS_W bool writeOpticalFlow( const String& path, InputArray flow )
{
const int nChannels = 2;
Mat input = flow.getMat();
if ( input.channels() != nChannels || input.depth() != CV_32F || path.length() == 0 )
return false;
std::ofstream file(path.c_str(), std::ofstream::binary);
if ( !file.good() )
return false;
int nRows, nCols;
nRows = (int) input.size().height;
nCols = (int) input.size().width;
const int headerSize = 12;
char header[headerSize];
memcpy(header, FLOW_TAG_STRING, 4);
// size of ints is known - has been asserted in the current function
memcpy(header + 4, reinterpret_cast<const char*>(&nCols), sizeof(nCols));
memcpy(header + 8, reinterpret_cast<const char*>(&nRows), sizeof(nRows));
file.write(header, headerSize);
if ( !file.good() )
return false;
int row;
char* p;
for ( row = 0; row < nRows; row++ )
{
p = input.ptr<char>(row);
file.write(p, nCols * nChannels * sizeof(float));
if ( !file.good() )
return false;
}
file.close();
return true;
}
}

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../test_precomp.hpp"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test { namespace {
PARAM_TEST_CASE(OCL_DenseOpticalFlow_DIS, int)
{
int preset;
virtual void SetUp()
{
preset = GET_PARAM(0);
}
};
OCL_TEST_P(OCL_DenseOpticalFlow_DIS, Mat)
{
Mat frame1, frame2, GT;
frame1 = imread(TS::ptr()->get_data_path() + "optflow/RubberWhale1.png");
frame2 = imread(TS::ptr()->get_data_path() + "optflow/RubberWhale2.png");
CV_Assert(!frame1.empty() && !frame2.empty());
cvtColor(frame1, frame1, COLOR_BGR2GRAY);
cvtColor(frame2, frame2, COLOR_BGR2GRAY);
Ptr<DenseOpticalFlow> algo;
// iterate over presets:
for (int i = 0; i < cvtest::ocl::test_loop_times; i++)
{
Mat flow;
UMat ocl_flow;
algo = DISOpticalFlow::create(preset);
OCL_OFF(algo->calc(frame1, frame2, flow));
OCL_ON(algo->calc(frame1, frame2, ocl_flow));
ASSERT_EQ(flow.rows, ocl_flow.rows);
ASSERT_EQ(flow.cols, ocl_flow.cols);
EXPECT_MAT_SIMILAR(flow, ocl_flow, 6e-3);
}
}
OCL_INSTANTIATE_TEST_CASE_P(Video, OCL_DenseOpticalFlow_DIS,
Values(DISOpticalFlow::PRESET_ULTRAFAST,
DISOpticalFlow::PRESET_FAST,
DISOpticalFlow::PRESET_MEDIUM));
}} // namespace
#endif // HAVE_OPENCL

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../test_precomp.hpp"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
/////////////////////////////////////////////////////////////////////////////////////////////////
// Optical_flow_tvl1
namespace
{
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
IMPLEMENT_PARAM_CLASS(MedianFiltering, int)
IMPLEMENT_PARAM_CLASS(ScaleStep, double)
}
PARAM_TEST_CASE(OpticalFlowTVL1, UseInitFlow, MedianFiltering, ScaleStep)
{
bool useInitFlow;
int medianFiltering;
double scaleStep;
virtual void SetUp()
{
useInitFlow = GET_PARAM(0);
medianFiltering = GET_PARAM(1);
scaleStep = GET_PARAM(2);
}
};
OCL_TEST_P(OpticalFlowTVL1, Mat)
{
cv::Mat frame0 = readImage("optflow/RubberWhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("optflow/RubberWhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
cv::Mat flow; cv::UMat uflow;
//create algorithm
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
//set parameters
alg->setScaleStep(scaleStep);
alg->setMedianFiltering(medianFiltering);
//create initial flow as result of algorithm calculation
if (useInitFlow)
{
OCL_ON(alg->calc(frame0, frame1, uflow));
uflow.copyTo(flow);
}
//set flag to use initial flow as it is ready to use
alg->setUseInitialFlow(useInitFlow);
OCL_OFF(alg->calc(frame0, frame1, flow));
OCL_ON(alg->calc(frame0, frame1, uflow));
EXPECT_MAT_SIMILAR(flow, uflow, 1e-2);
}
OCL_INSTANTIATE_TEST_CASE_P(Video, OpticalFlowTVL1,
Combine(
Values(UseInitFlow(false), UseInitFlow(true)),
Values(MedianFiltering(3), MedianFiltering(-1)),
Values(ScaleStep(0.3),ScaleStep(0.5))
)
);
} } // namespace opencv_test::ocl
#endif // HAVE_OPENCL

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
static string getDataDir() { return TS::ptr()->get_data_path(); }
static string getRubberWhaleFrame1() { return getDataDir() + "optflow/RubberWhale1.png"; }
static string getRubberWhaleFrame2() { return getDataDir() + "optflow/RubberWhale2.png"; }
static string getRubberWhaleGroundTruth() { return getDataDir() + "optflow/RubberWhale.flo"; }
static bool isFlowCorrect(float u) { return !cvIsNaN(u) && (fabs(u) < 1e9); }
static float calcRMSE(Mat flow1, Mat flow2)
{
float sum = 0;
int counter = 0;
const int rows = flow1.rows;
const int cols = flow1.cols;
for (int y = 0; y < rows; ++y)
{
for (int x = 0; x < cols; ++x)
{
Vec2f flow1_at_point = flow1.at<Vec2f>(y, x);
Vec2f flow2_at_point = flow2.at<Vec2f>(y, x);
float u1 = flow1_at_point[0];
float v1 = flow1_at_point[1];
float u2 = flow2_at_point[0];
float v2 = flow2_at_point[1];
if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2))
{
sum += (u1 - u2) * (u1 - u2) + (v1 - v2) * (v1 - v2);
counter++;
}
}
}
return (float)sqrt(sum / (1e-9 + counter));
}
bool readRubberWhale(Mat &dst_frame_1, Mat &dst_frame_2, Mat &dst_GT)
{
const string frame1_path = getRubberWhaleFrame1();
const string frame2_path = getRubberWhaleFrame2();
const string gt_flow_path = getRubberWhaleGroundTruth();
dst_frame_1 = imread(frame1_path);
dst_frame_2 = imread(frame2_path);
dst_GT = readOpticalFlow(gt_flow_path);
if (dst_frame_1.empty() || dst_frame_2.empty() || dst_GT.empty())
return false;
else
return true;
}
TEST(DenseOpticalFlow_DIS, ReferenceAccuracy)
{
Mat frame1, frame2, GT;
ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
int presets[] = {DISOpticalFlow::PRESET_ULTRAFAST, DISOpticalFlow::PRESET_FAST, DISOpticalFlow::PRESET_MEDIUM};
float target_RMSE[] = {0.86f, 0.74f, 0.49f};
cvtColor(frame1, frame1, COLOR_BGR2GRAY);
cvtColor(frame2, frame2, COLOR_BGR2GRAY);
Ptr<DenseOpticalFlow> algo;
// iterate over presets:
for (int i = 0; i < 3; i++)
{
Mat flow;
algo = DISOpticalFlow::create(presets[i]);
algo->calc(frame1, frame2, flow);
ASSERT_EQ(GT.rows, flow.rows);
ASSERT_EQ(GT.cols, flow.cols);
EXPECT_LE(calcRMSE(GT, flow), target_RMSE[i]);
}
}
TEST(DenseOpticalFlow_VariationalRefinement, ReferenceAccuracy)
{
Mat frame1, frame2, GT;
ASSERT_TRUE(readRubberWhale(frame1, frame2, GT));
float target_RMSE = 0.86f;
cvtColor(frame1, frame1, COLOR_BGR2GRAY);
cvtColor(frame2, frame2, COLOR_BGR2GRAY);
Ptr<VariationalRefinement> var_ref;
var_ref = VariationalRefinement::create();
var_ref->setAlpha(20.0f);
var_ref->setDelta(5.0f);
var_ref->setGamma(10.0f);
var_ref->setSorIterations(25);
var_ref->setFixedPointIterations(25);
Mat flow(frame1.size(), CV_32FC2);
flow.setTo(0.0f);
var_ref->calc(frame1, frame2, flow);
ASSERT_EQ(GT.rows, flow.rows);
ASSERT_EQ(GT.cols, flow.cols);
EXPECT_LE(calcRMSE(GT, flow), target_RMSE);
}
}} // namespace

160
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
typedef tuple<Size> OFParams;
typedef TestWithParam<OFParams> DenseOpticalFlow_DIS;
typedef TestWithParam<OFParams> DenseOpticalFlow_VariationalRefinement;
TEST_P(DenseOpticalFlow_DIS, MultithreadReproducibility)
{
double MAX_DIF = 0.01;
double MAX_MEAN_DIF = 0.001;
int loopsCount = 2;
RNG rng(0);
OFParams params = GetParam();
Size size = get<0>(params);
int nThreads = cv::getNumThreads();
if (nThreads == 1)
throw SkipTestException("Single thread environment");
for (int iter = 0; iter <= loopsCount; iter++)
{
Mat frame1(size, CV_8U);
randu(frame1, 0, 255);
Mat frame2(size, CV_8U);
randu(frame2, 0, 255);
Ptr<DISOpticalFlow> algo = DISOpticalFlow::create();
int psz = rng.uniform(4, 16);
int pstr = rng.uniform(1, psz - 1);
int grad_iter = rng.uniform(1, 64);
int var_iter = rng.uniform(0, 10);
bool use_mean_normalization = !!rng.uniform(0, 2);
bool use_spatial_propagation = !!rng.uniform(0, 2);
algo->setFinestScale(0);
algo->setPatchSize(psz);
algo->setPatchStride(pstr);
algo->setGradientDescentIterations(grad_iter);
algo->setVariationalRefinementIterations(var_iter);
algo->setUseMeanNormalization(use_mean_normalization);
algo->setUseSpatialPropagation(use_spatial_propagation);
cv::setNumThreads(nThreads);
Mat resMultiThread;
algo->calc(frame1, frame2, resMultiThread);
cv::setNumThreads(1);
Mat resSingleThread;
algo->calc(frame1, frame2, resSingleThread);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF * frame1.total());
// resulting flow should be within the frame bounds:
double min_val, max_val;
minMaxLoc(resMultiThread, &min_val, &max_val);
EXPECT_LE(abs(min_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
EXPECT_LE(abs(max_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
}
}
INSTANTIATE_TEST_CASE_P(FullSet, DenseOpticalFlow_DIS, Values(szODD, szQVGA));
TEST_P(DenseOpticalFlow_VariationalRefinement, MultithreadReproducibility)
{
double MAX_DIF = 0.01;
double MAX_MEAN_DIF = 0.001;
float input_flow_rad = 5.0;
int loopsCount = 2;
RNG rng(0);
OFParams params = GetParam();
Size size = get<0>(params);
int nThreads = cv::getNumThreads();
if (nThreads == 1)
throw SkipTestException("Single thread environment");
for (int iter = 0; iter <= loopsCount; iter++)
{
Mat frame1(size, CV_8U);
randu(frame1, 0, 255);
Mat frame2(size, CV_8U);
randu(frame2, 0, 255);
Mat flow(size, CV_32FC2);
randu(flow, -input_flow_rad, input_flow_rad);
Ptr<VariationalRefinement> var = VariationalRefinement::create();
var->setAlpha(rng.uniform(1.0f, 100.0f));
var->setGamma(rng.uniform(0.1f, 10.0f));
var->setDelta(rng.uniform(0.1f, 10.0f));
var->setSorIterations(rng.uniform(1, 20));
var->setFixedPointIterations(rng.uniform(1, 20));
var->setOmega(rng.uniform(1.01f, 1.99f));
cv::setNumThreads(nThreads);
Mat resMultiThread;
flow.copyTo(resMultiThread);
var->calc(frame1, frame2, resMultiThread);
cv::setNumThreads(1);
Mat resSingleThread;
flow.copyTo(resSingleThread);
var->calc(frame1, frame2, resSingleThread);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF * frame1.total());
// resulting flow should be within the frame bounds:
double min_val, max_val;
minMaxLoc(resMultiThread, &min_val, &max_val);
EXPECT_LE(abs(min_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
EXPECT_LE(abs(max_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
}
}
INSTANTIATE_TEST_CASE_P(FullSet, DenseOpticalFlow_VariationalRefinement, Values(szODD, szQVGA));
}} // namespace

5
modules/video/test/test_precomp.hpp
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@ -6,5 +6,10 @@
#include "opencv2/ts.hpp"
#include "opencv2/video.hpp"
#include <opencv2/ts/ts_perf.hpp>
namespace opencv_test {
using namespace perf;
}
#endif

173
modules/video/test/test_tvl1optflow.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
//#define DUMP
// first four bytes, should be the same in little endian
const float FLO_TAG_FLOAT = 202021.25f; // check for this when READING the file
#ifdef DUMP
// binary file format for flow data specified here:
// http://vision.middlebury.edu/flow/data/
void writeOpticalFlowToFile(const Mat_<Point2f>& flow, const string& fileName)
{
const char FLO_TAG_STRING[] = "PIEH"; // use this when WRITING the file
ofstream file(fileName.c_str(), ios_base::binary);
file << FLO_TAG_STRING;
file.write((const char*) &flow.cols, sizeof(int));
file.write((const char*) &flow.rows, sizeof(int));
for (int i = 0; i < flow.rows; ++i)
{
for (int j = 0; j < flow.cols; ++j)
{
const Point2f u = flow(i, j);
file.write((const char*) &u.x, sizeof(float));
file.write((const char*) &u.y, sizeof(float));
}
}
}
#endif
// binary file format for flow data specified here:
// http://vision.middlebury.edu/flow/data/
void readOpticalFlowFromFile(Mat_<Point2f>& flow, const string& fileName)
{
std::ifstream file(fileName.c_str(), std::ios_base::binary);
float tag;
file.read((char*) &tag, sizeof(float));
CV_Assert( tag == FLO_TAG_FLOAT );
Size size;
file.read((char*) &size.width, sizeof(int));
file.read((char*) &size.height, sizeof(int));
flow.create(size);
for (int i = 0; i < flow.rows; ++i)
{
for (int j = 0; j < flow.cols; ++j)
{
Point2f u;
file.read((char*) &u.x, sizeof(float));
file.read((char*) &u.y, sizeof(float));
flow(i, j) = u;
}
}
file.close();
}
bool isFlowCorrect(Point2f u)
{
return !cvIsNaN(u.x) && !cvIsNaN(u.y) && (fabs(u.x) < 1e9) && (fabs(u.y) < 1e9);
}
void check(const Mat_<Point2f>& gold, const Mat_<Point2f>& flow, double threshold = 0.1, double expectedAccuracy = 0.95)
{
threshold = threshold*threshold;
size_t gold_counter = 0;
size_t valid_counter = 0;
for (int i = 0; i < gold.rows; ++i)
{
for (int j = 0; j < gold.cols; ++j)
{
const Point2f u1 = gold(i, j);
const Point2f u2 = flow(i, j);
if (isFlowCorrect(u1))
{
gold_counter++;
if (isFlowCorrect(u2))
{
const Point2f diff = u1 - u2;
double err = diff.ddot(diff);
if (err <= threshold)
valid_counter++;
}
}
}
}
EXPECT_GE(valid_counter, expectedAccuracy * gold_counter);
}
TEST(Video_calcOpticalFlowDual_TVL1, Regression)
{
const string frame1_path = TS::ptr()->get_data_path() + "optflow/RubberWhale1.png";
const string frame2_path = TS::ptr()->get_data_path() + "optflow/RubberWhale2.png";
const string gold_flow_path = TS::ptr()->get_data_path() + "optflow/tvl1_flow.flo";
Mat frame1 = imread(frame1_path, IMREAD_GRAYSCALE);
Mat frame2 = imread(frame2_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
ASSERT_FALSE(frame2.empty());
Mat_<Point2f> flow;
Ptr<DualTVL1OpticalFlow> tvl1 = cv::DualTVL1OpticalFlow::create();
tvl1->calc(frame1, frame2, flow);
#ifdef DUMP
writeOpticalFlowToFile(flow, gold_flow_path);
#else
Mat_<Point2f> gold;
readOpticalFlowFromFile(gold, gold_flow_path);
ASSERT_EQ(gold.rows, flow.rows);
ASSERT_EQ(gold.cols, flow.cols);
check(gold, flow);
#endif
}
}} // namespace

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#include "opencv2/core/utility.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/video.hpp"
using namespace std;
using namespace cv;
static void help()
{
printf("Usage: dis_optflow <video_file>\n");
}
int main(int argc, char **argv)
{
VideoCapture cap;
if (argc < 2)
{
help();
exit(1);
}
cap.open(argv[1]);
if(!cap.isOpened())
{
printf("ERROR: Cannot open file %s\n", argv[1]);
return -1;
}
Mat prevgray, gray, rgb, frame;
Mat flow, flow_uv[2];
Mat mag, ang;
Mat hsv_split[3], hsv;
char ret;
namedWindow("flow", 1);
namedWindow("orig", 1);
Ptr<DenseOpticalFlow> algorithm = DISOpticalFlow::create(DISOpticalFlow::PRESET_MEDIUM);
while(true)
{
cap >> frame;
if (frame.empty())
break;
cvtColor(frame, gray, COLOR_BGR2GRAY);
if (!prevgray.empty())
{
algorithm->calc(prevgray, gray, flow);
split(flow, flow_uv);
multiply(flow_uv[1], -1, flow_uv[1]);
cartToPolar(flow_uv[0], flow_uv[1], mag, ang, true);
normalize(mag, mag, 0, 1, NORM_MINMAX);
hsv_split[0] = ang;
hsv_split[1] = mag;
hsv_split[2] = Mat::ones(ang.size(), ang.type());
merge(hsv_split, 3, hsv);
cvtColor(hsv, rgb, COLOR_HSV2BGR);
imshow("flow", rgb);
imshow("orig", frame);
}
if ((ret = (char)waitKey(20)) > 0)
break;
std::swap(prevgray, gray);
}
return 0;
}

204
samples/cpp/tvl1_optical_flow.cpp
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#include <iostream>
#include <fstream>
#include <opencv2/core/utility.hpp>
#include "opencv2/video.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
using namespace cv;
using namespace std;
inline bool isFlowCorrect(Point2f u)
{
return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9;
}
static Vec3b computeColor(float fx, float fy)
{
static bool first = true;
// relative lengths of color transitions:
// these are chosen based on perceptual similarity
// (e.g. one can distinguish more shades between red and yellow
// than between yellow and green)
const int RY = 15;
const int YG = 6;
const int GC = 4;
const int CB = 11;
const int BM = 13;
const int MR = 6;
const int NCOLS = RY + YG + GC + CB + BM + MR;
static Vec3i colorWheel[NCOLS];
if (first)
{
int k = 0;
for (int i = 0; i < RY; ++i, ++k)
colorWheel[k] = Vec3i(255, 255 * i / RY, 0);
for (int i = 0; i < YG; ++i, ++k)
colorWheel[k] = Vec3i(255 - 255 * i / YG, 255, 0);
for (int i = 0; i < GC; ++i, ++k)
colorWheel[k] = Vec3i(0, 255, 255 * i / GC);
for (int i = 0; i < CB; ++i, ++k)
colorWheel[k] = Vec3i(0, 255 - 255 * i / CB, 255);
for (int i = 0; i < BM; ++i, ++k)
colorWheel[k] = Vec3i(255 * i / BM, 0, 255);
for (int i = 0; i < MR; ++i, ++k)
colorWheel[k] = Vec3i(255, 0, 255 - 255 * i / MR);
first = false;
}
const float rad = sqrt(fx * fx + fy * fy);
const float a = atan2(-fy, -fx) / (float)CV_PI;
const float fk = (a + 1.0f) / 2.0f * (NCOLS - 1);
const int k0 = static_cast<int>(fk);
const int k1 = (k0 + 1) % NCOLS;
const float f = fk - k0;
Vec3b pix;
for (int b = 0; b < 3; b++)
{
const float col0 = colorWheel[k0][b] / 255.f;
const float col1 = colorWheel[k1][b] / 255.f;
float col = (1 - f) * col0 + f * col1;
if (rad <= 1)
col = 1 - rad * (1 - col); // increase saturation with radius
else
col *= .75; // out of range
pix[2 - b] = static_cast<uchar>(255.f * col);
}
return pix;
}
static void drawOpticalFlow(const Mat_<Point2f>& flow, Mat& dst, float maxmotion = -1)
{
dst.create(flow.size(), CV_8UC3);
dst.setTo(Scalar::all(0));
// determine motion range:
float maxrad = maxmotion;
if (maxmotion <= 0)
{
maxrad = 1;
for (int y = 0; y < flow.rows; ++y)
{
for (int x = 0; x < flow.cols; ++x)
{
Point2f u = flow(y, x);
if (!isFlowCorrect(u))
continue;
maxrad = max(maxrad, sqrt(u.x * u.x + u.y * u.y));
}
}
}
for (int y = 0; y < flow.rows; ++y)
{
for (int x = 0; x < flow.cols; ++x)
{
Point2f u = flow(y, x);
if (isFlowCorrect(u))
dst.at<Vec3b>(y, x) = computeColor(u.x / maxrad, u.y / maxrad);
}
}
}
// binary file format for flow data specified here:
// http://vision.middlebury.edu/flow/data/
static void writeOpticalFlowToFile(const Mat_<Point2f>& flow, const string& fileName)
{
static const char FLO_TAG_STRING[] = "PIEH";
ofstream file(fileName.c_str(), ios_base::binary);
file << FLO_TAG_STRING;
file.write((const char*) &flow.cols, sizeof(int));
file.write((const char*) &flow.rows, sizeof(int));
for (int i = 0; i < flow.rows; ++i)
{
for (int j = 0; j < flow.cols; ++j)
{
const Point2f u = flow(i, j);
file.write((const char*) &u.x, sizeof(float));
file.write((const char*) &u.y, sizeof(float));
}
}
}
int main(int argc, const char* argv[])
{
cv::CommandLineParser parser(argc, argv, "{help h || show help message}"
"{ @frame0 | | frame 0}{ @frame1 | | frame 1}{ @output | | output flow}");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
string frame0_name = parser.get<string>("@frame0");
string frame1_name = parser.get<string>("@frame1");
string file = parser.get<string>("@output");
if (frame0_name.empty() || frame1_name.empty() || file.empty())
{
cerr << "Usage : " << argv[0] << " [<frame0>] [<frame1>] [<output_flow>]" << endl;
return -1;
}
Mat frame0 = imread(frame0_name, IMREAD_GRAYSCALE);
Mat frame1 = imread(frame1_name, IMREAD_GRAYSCALE);
if (frame0.empty())
{
cerr << "Can't open image [" << parser.get<string>("frame0") << "]" << endl;
return -1;
}
if (frame1.empty())
{
cerr << "Can't open image [" << parser.get<string>("frame1") << "]" << endl;
return -1;
}
if (frame1.size() != frame0.size())
{
cerr << "Images should be of equal sizes" << endl;
return -1;
}
Mat_<Point2f> flow;
Ptr<DualTVL1OpticalFlow> tvl1 = cv::DualTVL1OpticalFlow::create();
const double start = (double)getTickCount();
tvl1->calc(frame0, frame1, flow);
const double timeSec = (getTickCount() - start) / getTickFrequency();
cout << "calcOpticalFlowDual_TVL1 : " << timeSec << " sec" << endl;
Mat out;
drawOpticalFlow(flow, out);
if (!file.empty())
writeOpticalFlowToFile(flow, file);
imshow("Flow", out);
waitKey();
return 0;
}

114
samples/python/dis_opt_flow.py Executable file
View File

@ -0,0 +1,114 @@
#!/usr/bin/env python
'''
example to show optical flow estimation using DISOpticalFlow
USAGE: dis_opt_flow.py [<video_source>]
Keys:
1 - toggle HSV flow visualization
2 - toggle glitch
3 - toggle spatial propagation of flow vectors
4 - toggle temporal propagation of flow vectors
ESC - exit
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
import video
def draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
fx, fy = flow[y,x].T
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
cv.polylines(vis, lines, 0, (0, 255, 0))
for (x1, y1), (x2, y2) in lines:
cv.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
return vis
def draw_hsv(flow):
h, w = flow.shape[:2]
fx, fy = flow[:,:,0], flow[:,:,1]
ang = np.arctan2(fy, fx) + np.pi
v = np.sqrt(fx*fx+fy*fy)
hsv = np.zeros((h, w, 3), np.uint8)
hsv[...,0] = ang*(180/np.pi/2)
hsv[...,1] = 255
hsv[...,2] = np.minimum(v*4, 255)
bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
return bgr
def warp_flow(img, flow):
h, w = flow.shape[:2]
flow = -flow
flow[:,:,0] += np.arange(w)
flow[:,:,1] += np.arange(h)[:,np.newaxis]
res = cv.remap(img, flow, None, cv.INTER_LINEAR)
return res
if __name__ == '__main__':
import sys
print(__doc__)
try:
fn = sys.argv[1]
except IndexError:
fn = 0
cam = video.create_capture(fn)
ret, prev = cam.read()
prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)
show_hsv = False
show_glitch = False
use_spatial_propagation = False
use_temporal_propagation = True
cur_glitch = prev.copy()
inst = cv.DISOpticalFlow.create(cv.DISOPTICAL_FLOW_PRESET_MEDIUM)
inst.setUseSpatialPropagation(use_spatial_propagation)
flow = None
while True:
ret, img = cam.read()
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
if flow is not None and use_temporal_propagation:
#warp previous flow to get an initial approximation for the current flow:
flow = inst.calc(prevgray, gray, warp_flow(flow,flow))
else:
flow = inst.calc(prevgray, gray, None)
prevgray = gray
cv.imshow('flow', draw_flow(gray, flow))
if show_hsv:
cv.imshow('flow HSV', draw_hsv(flow))
if show_glitch:
cur_glitch = warp_flow(cur_glitch, flow)
cv.imshow('glitch', cur_glitch)
ch = 0xFF & cv.waitKey(5)
if ch == 27:
break
if ch == ord('1'):
show_hsv = not show_hsv
print('HSV flow visualization is', ['off', 'on'][show_hsv])
if ch == ord('2'):
show_glitch = not show_glitch
if show_glitch:
cur_glitch = img.copy()
print('glitch is', ['off', 'on'][show_glitch])
if ch == ord('3'):
use_spatial_propagation = not use_spatial_propagation
inst.setUseSpatialPropagation(use_spatial_propagation)
print('spatial propagation is', ['off', 'on'][use_spatial_propagation])
if ch == ord('4'):
use_temporal_propagation = not use_temporal_propagation
print('temporal propagation is', ['off', 'on'][use_temporal_propagation])
cv.destroyAllWindows()

10
samples/tapi/dense_optical_flow.cpp
View File

@ -50,7 +50,7 @@ int main(int argc, const char* argv[])
const char* keys =
"{ h help | | print help message }"
"{ c camera | 0 | capture video from camera (device index starting from 0) }"
"{ a algorithm | fb | algorithm (supported: 'fb', 'tvl')}"
"{ a algorithm | fb | algorithm (supported: 'fb', 'dis')}"
"{ m cpu | | run without OpenCL }"
"{ v video | | use video as input }"
"{ o original | | use original frame size (do not resize to 640x480)}"
@ -84,11 +84,11 @@ int main(int argc, const char* argv[])
return 2;
}
cv::Ptr<cv::DenseOpticalFlow> alg;
Ptr<DenseOpticalFlow> alg;
if (algorithm == "fb")
alg = cv::FarnebackOpticalFlow::create();
else if (algorithm == "tvl")
alg = cv::DualTVL1OpticalFlow::create();
alg = FarnebackOpticalFlow::create();
else if (algorithm == "dis")
alg = DISOpticalFlow::create(DISOpticalFlow::PRESET_FAST);
else
{
cout << "Invalid algorithm: " << algorithm << endl;